Hyperspectral Fusion Using Weighted Nonlocal Vector Total Variation

Abstract

Hyperspectral (HS) images have high spectral but low spatial resolutions, while multispectral (MS) images, on the other hand, have low spectral but high spatial resolutions. In HS–MS fusion, the HS and MS images are combined to obtain a single image with high spatial and spectral resolutions. Such images are typically textured and exhibit repetitive structures. To exploit this prior, we propose a nonlocal weighted total-variation regularizer. The novelty of the design is that the following hold: 1) we use a weighted norm, where the weights are derived from the MS image and 2) pixel variations over nonlocal neighborhoods are considered. We incorporate the regularizer into a standard convex optimization framework involving quadratic data-fidelity terms. We develop an efficient ADMM algorithm for solving this optimization problem—the novelty in this regard is that we use a variable splitting technique that results in the closed-form solutions of the ADMM subproblems. We report results on standard datasets demonstrating that the proposed regularizer can recover fine textures (as opposed to local pixel-based methods) and outperform the state-of-the-art methods.